A problem related to the development of an algorithm designed to find an architecture of artificial neural network used for black-box modelling of dynamic systems with time delays has been addressed in this paper. The proposed algorithm is based on a well-known NeuroEvolution of Augmenting Topologies (NEAT) algorithm. The NEAT algorithm has been adjusted by allowing additional connections within an artificial neural network and developing original specialised evolutionary operators. This resulted in a compromise between the size of neural network and its accuracy in capturing the response of the mathematical model under which it has been learnt. The research involved an extended validation study based on data generated from a mathematical model of an exemplary system as well as the fast processes occurring in a pressurised water nuclear reactor. The obtaining simulation results demonstrate the high effectiveness of the devised neural (black-box) models of dynamic systems with time delays.

翻译：本文提出了一种算法，旨在寻找用于时滞动态系统黑箱建模的人工神经网络架构。该算法以著名的增强拓扑神经进化（NEAT）算法为基础，通过允许人工神经网络内增加额外连接并开发原创的专用进化算子对其进行改进。这使得网络规模与其学习所依据数学模型响应捕捉精度之间达到了折中。研究基于一个示例系统数学模型生成的数据以及压水核反应堆中发生的快过程进行了扩展验证。仿真结果表明，所设计的时滞动态系统神经（黑箱）模型具有很高的有效性。